Sofia D. Panteliou

MODELING OF SOLAR DOMESTIC WATER HEATING SYSTEMS USING ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can be trained to predict results from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly useful in system modeling and for system identification. The objective of this work was to train an ANN to learn to predict the useful energy extracted and the temperature rise in the stored water of solar domestic water heating (SDHW) systems with the minimum of input data. An ANN has been trained based on 30 known cases of systems, 22 varying from collector areas between 1.81 m and 4.38 m . Open and closed systems have been considered both with horizontal and vertical storage tanks. In addition to the above, an attempt was made to consider a large variety of weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were the collector area, storage tank heat loss coefficient (U-value), tank type, storage volume, type of system, and ten readings from real experiments of total daily solar radiation, mean ambient air temperature, and the water temperature in the storage tank at the beginning of a day. The network output is the useful energy extracted from the system and the temperature rise in the stored water. The 2 statistical R -value obtained for the training data set was equal to 0.9722 and 0.9751 for the two output parameters respectively. Unknown data were subsequently used to investigate the accuracy of prediction. These include systems considered for the training of the network at different weather conditions and completely unknown systems. Predictions within 7.1% and 9.7% were obtained respectively. These results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored water. The advantages of this approach compared to the conventional algorithmic methods are the speed, the simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network. Additionally, actual weather data have been used for the training of the network, which leads to more realistic results as compared to other modeling programs, which rely on TMY data that are not necessarily similar to the actual environment in which a system operates.

Artificial neural networks used for the performance prediction of a thermosiphon solar water heater

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly effective in system modelling as well as for system identification. The objective of this work is to train an artificial neural network (ANN) to learn to predict the performance of a thermosiphon solar domestic water heating system. This performance is measured in terms of the useful energy extracted and of the stored water temperature rise. An ANN has been trained using performance data for four types of systems, all employing the same collector panel under varying weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were, the storage tank heat loss coefficient (U-value), the type of system (open or closed), the storage volume, and a total of fifty-four readings from real experiments of total daily solar radiation, total daily diffuse radiation, ambient air temperature, and the water temperature in storage tank at the beginning of the day. The network output is the useful energy extracted from the system and the water temperature rise. The statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9914 and 0.9808 for the two output parameters respectively. Both values are satisfactory because the closer R2-value is to unity the better is the mapping. Unknown data for all four systems were subsequently used to investigate the accuracy of prediction. These include performance data for the systems considered for the training of the network at different weather conditions. Predictions with maximum deviations of 1 MJ and 2.2°C were obtained respectively. Random data were also used both with the performance equations obtained from the experimental measurements and with the artificial neural network to predict the above two parameters. The predicted values thus obtained were very comparable. These results indicate that the proposed method can successfully be used for the estimation of the performance of the particular thermosiphon system at any of the different types of configuration used here. The greatest advantage of the present model is the capacity of the network to learn from examples and thus gradually improve its performance. This is done by embedding experimental knowledge in the network.

Thermosiphon solar domestic water heating systems: long-term performance prediction using artificial neural networks

Abstract The objective of this work is to use artificial neural networks (ANN) for the long-term performance prediction of thermosiphonic type solar domestic water heating (SDWH) systems. Thirty SDWH systems have been tested and modelled according to the procedures outlined in the standard ISO 9459-2 at three locations in Greece. From these, data from 27 of the systems were used for training and testing the network while data from the remaining three were used for validation. Two ANNs have been trained using the monthly data produced by the modeling program supplied with the standard ISO 9459-2. Different networks were used depending on the nature of the required output, which is different in each case. The first network was trained to estimate the solar energy output of the system for a draw-off quantity equal to the storage tank capacity (at the end of the solar energy collection period) and the second one was trained to estimate the solar energy output of the system and the average quantity of hot water per month at demand temperatures of 35 and 40°C. The collector areas of the considered systems were varying between 1.81 m2 and 4.38 m2. Open and closed thermosiphonic systems have been considered both with horizontal and vertical storage tanks. In this way the networks were trained to accept and handle a number of unusual cases. The input data in both networks are similar to the ones used in the program supplied with the standard. These were the size and performance characteristics of each system and various climatic data. In the second network the demand temperature was also used as input. For the first network the statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9993. For the second network the R2-value for the two output parameters was equal to 0.9848 and 0.9926, respectively. Unknown data were subsequently used to investigate the accuracy of prediction and R2-values equal to 0.9913 for the first network and 0.9733 and 0.9940 for the second were obtained. These results indicate that the proposed method can successfully be used for the prediction of the solar energy output of the system for a draw-off equal to the volume of the storage tank or for the solar energy output of the system and the average quantity of the hot water per month for the two demand water temperatures considered.

Modal damping for monitoring bone integrity and osteoporosis

We applied a noninvasive method to assess bone structural integrity. The method is based on the measurement of the dynamic characteristics of the bone (quality factor and modal damping factor) by applying vibration excitation in the range of acoustic frequencies, in the form of an acoustic sweep signal. Excised sheep femora were tested to detect changes in modal damping, density (kg/m3), bone mineral density (kg/m2) and bone mineral (hydroxyapatite) percentage. The changes were recorded after each time of chemical treatment of the bones performed to gradually cause mineral removal, thus simulating osteoporosis. It was shown that the change in quality factor and damping was in all cases on average equal or greater to the change in all other measured characteristics, thus strengthening the potential of the proposed method to become a valuable assessment tool for monitoring bone integrity and osteoporosis.

Direct and inverse interpolation for Jacobian elliptic functions, zeta function of Jacobi and complete elliptic integrals of the second kind

Abstract Computing the value of the Jacobian elliptic functions, given the argument u and the parameter m , is a problem, whose solution can be found either tabulated in tables of elliptic functions [1] or by use of existing software, such as Mathematica, etc. The inverse problem, finding the argument, given the Jacobian elliptic function and the parameter m , is a problem whose solution is found only in tables of elliptic functions. Standard polynomial inverse interpolation procedures fail, due to ill conditioning of the system of linear equations of the unknowns. In this paper, we describe a numerical procedure for inverse interpolation which gives good results in the computation of the argument of the Jacobian elliptic function given the Jacobian elliptic function and the parameter. Also, a direct interpolation is described which gives the Zeta function of Jacobi and the complete elliptic integral of the second kind given the argument and the parameter. These new interpolation procedures are important in problems involving cavities or inclusions of ellipsoidal shape encountered in the mechanical design of bearings, filters and composite materials. They are also important in the modelling of porosity of bones. This porosity may lead to osteoporosis, a disease which affects bone mineral density in humans with bad consequences.

Use of expert systems for the selection and the design of solar domestic hot water systems

The aim of this article is the study of the application of expert systems to a mechanical engineering research domain with practical and commercial interest, such as design and manufacturing of Solar Domestic Hot Water (SDHW) Systems. The issues studied were the selection and the design of SDHW systems. The application of an expert system was explored. Frame and class formalism was used for knowledge representation together with forward and backward chaining techniques for drawing conclusions and utilizing the accumulated information present. The appropriate computer program was developed to yield the selection of SDHW systems using the software tool LEONARDO 3.0 (1989), an integrated environment for the development of expert systems. The developed program was tested with data according to the Greek standard ELOT corresponding to the ISO/DIS 9459-2 and it performed successfully for 21 SDHW systems available on the Greek market. Apart from the possibility of selection of a SDHW system, the program also supports the facility for updating its knowledge base with new data so that it can be adapted to changes appearing on the market. The program proved to be functional and user friendly to a high degree.

Low-Frequency Acoustic Sweep Monitoring of Bone Integrity and Osteoporosis

We developed a noninvasive method to evaluate bone structural integrity. It is based on the measurement of the dynamic characteristics of the bone using sweeping sound excitation in the range of acoustic frequencies. The Quality Factor (a measure of material damping) has been used as an indicator of the tendency of the bone to fracture. Results of animal studies have supported this hypothesis since linear correlations were observed between bone density, quality factor, and impact strength. A vibration excitation in the form of an acoustic sweep signal is applied to a bone to measure the quality factor. Rat bones were tested, obtained from animals with osteoporosis age-dependent (tested in vitro) or ovariectomy-induced (tested in vivo), and compared with bones of healthy (control) rats. The change in damping was, on average, equal or greater to the change in density. Moreover, excellent correlation of the quality factor was obtained with bone fracture energy measured with an impact test. During a vibration cycle, the changing strain results in temperature changes due to the reciprocity of temperature and strain. Nonreversible conduction of heat due to the unequal temperature change results in entropy production that is enhanced due to the stress concentration about the voids associated with bone porosity. Damping is a measure of the production of entropy. Its measure, the quality factor, represents a potentially useful tool for monitoring bone integrity, which is deteriorating in diseases characterized by disruption of the trabecular architecture, such as osteoporosis. A computational model yielded results that are in good correlation with the experimental results.

Alteration in serum leptin correlates with alterations in serum N-telopeptide of collagen type I and serum osteocalcin during the progression of osteoporosis in ovariectomized rats

Abstract The role of leptin during the progression of osteoporosis was investigated in ovariectomized rats by correlation of serum leptin levels with N-telopeptide of collagen type I (NTx) and osteocalcin levels before ovariectomy and 20, 40 and 60days after the operation. Furthermore, peripheral quantitative computed tomography was used to confirm the development of severe osteoporosis in rats on day 60. The levels of NTx and osteocalcin were significantly increased on day 20 [61.9±5.4nM BCE (bone collagen equivalents) and 215.6±53.3ng/mL, respectively] in comparison to those before ovariectomy (41.3±1.7nM BCE and 60.4±10.9ng/mL). Accordingly, leptin was significantly elevated on day 20 (3033±661 vs. 606±346 pg/mL before ovariectomy). Bone markers and leptin levels remained constant up to day 40, while a slight, but not statistically significant, decrease was noted for osteocalcin and leptin on day 60. Although leptin and bone markers did not correlate before ovariectomy (r=0.09 for NTx and r=−0.05 for osteocalcin), strong correlation was observed at all time points after ovariectomy. The data obtained suggest that the alterations in serum leptin levels during the progression of osteoporosis in ovariectomized rats follow the alterations in bone markers.

Thermodynamic Damping in Porous Materials with Spherical Cavities

When a material is subjected to an alternating stress field, there are temperature fluctuations throughout its volume due to the thermoelastic effect. The resulting irreversible heat conduction leads to entropy production that in turn is the cause of thermoelastic damping. An analytical investigation of the entropy produced during a vibration cycle due to the reciprocity of temperature rise and strain yielded the change of the material damping factor as a function of the porosity of the material. A homogeneous, isotropic, elastic bar of cylindrical shape is considered with uniformly distributed spherical cavities under alternating uniform axial stress. The analytical calculation of the dynamic characteristics of the porous structure yielded the damping factor of the bar and the material damping factor. Exsperimental results on porous metals are in good correlation with an analysis.

Reliability of hot water solar systems in Greece

Ten thousand domestic hot water solar systems were surveyed in Greece to assess component and system reliability. Data concerning the functioning condition of the systems was collected, a computerized data base was established and statistical analysis was performed. This work is part of a solar system evaluation program within the European Community. Greece was selected due to the high concentration of solar collector systems and the fact that these systems have reached maturity, the average lifespan being five years.